Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations135038
Missing cells613
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.8 MiB
Average record size in memory751.8 B

Variable types

Text6
Categorical4
Numeric7

Alerts

2020 Census Tract is highly overall correlated with StateHigh correlation
Clean Alternative Fuel Vehicle (CAFV) Eligibility is highly overall correlated with Electric Range and 3 other fieldsHigh correlation
Electric Range is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 2 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 2 other fieldsHigh correlation
Legislative District is highly overall correlated with StateHigh correlation
Make is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 1 other fieldsHigh correlation
Model Year is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 1 other fieldsHigh correlation
Postal Code is highly overall correlated with StateHigh correlation
State is highly overall correlated with 2020 Census Tract and 2 other fieldsHigh correlation
State is highly imbalanced (99.4%)Imbalance
Postal Code is highly skewed (γ1 = -30.28086473)Skewed
2020 Census Tract is highly skewed (γ1 = -26.10258255)Skewed
DOL Vehicle ID has unique valuesUnique
Electric Range has 56983 (42.2%) zerosZeros
Base MSRP has 131612 (97.5%) zerosZeros

Reproduction

Analysis started2024-08-04 05:06:43.296755
Analysis finished2024-08-04 05:07:06.016655
Duration22.72 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct9059
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2024-08-04T10:37:06.372981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1350380
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1770 ?
Unique (%)1.3%

Sample

1st row5YJ3E1EA0K
2nd row1N4BZ1DV4N
3rd row5YJ3E1EA0L
4th row5YJ3E1EBXL
5th row5YJSA1CP0D
ValueCountFrequency (%)
5yjygdee9m 473
 
0.4%
5yjygdee0m 470
 
0.3%
5yjygdee7m 459
 
0.3%
5yjygdee8m 457
 
0.3%
7saygdee6p 454
 
0.3%
7saygdee2p 448
 
0.3%
7saygdeexp 442
 
0.3%
5yjygdee2m 441
 
0.3%
5yjygdeexm 438
 
0.3%
7saygdee7p 438
 
0.3%
Other values (9049) 130518
96.7%
2024-08-04T10:37:06.926068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 122239
 
9.1%
1 103650
 
7.7%
Y 79619
 
5.9%
A 78494
 
5.8%
J 76650
 
5.7%
5 71916
 
5.3%
3 57737
 
4.3%
N 51343
 
3.8%
G 50196
 
3.7%
P 50067
 
3.7%
Other values (24) 608469
45.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 916373
67.9%
Decimal Number 434007
32.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 122239
13.3%
Y 79619
 
8.7%
A 78494
 
8.6%
J 76650
 
8.4%
N 51343
 
5.6%
G 50196
 
5.5%
P 50067
 
5.5%
D 49237
 
5.4%
C 48788
 
5.3%
S 38744
 
4.2%
Other values (14) 270996
29.6%
Decimal Number
ValueCountFrequency (%)
1 103650
23.9%
5 71916
16.6%
3 57737
13.3%
4 39313
 
9.1%
0 36537
 
8.4%
7 34877
 
8.0%
6 31609
 
7.3%
2 28649
 
6.6%
8 16368
 
3.8%
9 13351
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 916373
67.9%
Common 434007
32.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 122239
13.3%
Y 79619
 
8.7%
A 78494
 
8.6%
J 76650
 
8.4%
N 51343
 
5.6%
G 50196
 
5.5%
P 50067
 
5.5%
D 49237
 
5.4%
C 48788
 
5.3%
S 38744
 
4.2%
Other values (14) 270996
29.6%
Common
ValueCountFrequency (%)
1 103650
23.9%
5 71916
16.6%
3 57737
13.3%
4 39313
 
9.1%
0 36537
 
8.4%
7 34877
 
8.0%
6 31609
 
7.3%
2 28649
 
6.6%
8 16368
 
3.8%
9 13351
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1350380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 122239
 
9.1%
1 103650
 
7.7%
Y 79619
 
5.9%
A 78494
 
5.8%
J 76650
 
5.7%
5 71916
 
5.3%
3 57737
 
4.3%
N 51343
 
3.8%
G 50196
 
3.7%
P 50067
 
3.7%
Other values (24) 608469
45.1%

County
Text

Distinct169
Distinct (%)0.1%
Missing8
Missing (%)< 0.1%
Memory size7.0 MiB
2024-08-04T10:37:07.181233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.4529216
Min length3

Characters and Unicode

Total characters736308
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)0.1%

Sample

1st rowThurston
2nd rowIsland
3rd rowSnohomish
4th rowKing
5th rowSnohomish
ValueCountFrequency (%)
king 70842
51.8%
snohomish 15258
 
11.2%
pierce 10410
 
7.6%
clark 7997
 
5.8%
thurston 4851
 
3.5%
kitsap 4461
 
3.3%
spokane 3326
 
2.4%
whatcom 3315
 
2.4%
benton 1688
 
1.2%
skagit 1504
 
1.1%
Other values (177) 13139
 
9.6%
2024-08-04T10:37:07.654549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 107219
14.6%
n 105625
14.3%
K 75984
10.3%
g 72868
9.9%
o 47062
 
6.4%
h 39783
 
5.4%
a 34062
 
4.6%
s 29598
 
4.0%
e 29533
 
4.0%
r 26624
 
3.6%
Other values (41) 167950
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 597752
81.2%
Uppercase Letter 136787
 
18.6%
Space Separator 1761
 
0.2%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 107219
17.9%
n 105625
17.7%
g 72868
12.2%
o 47062
7.9%
h 39783
 
6.7%
a 34062
 
5.7%
s 29598
 
5.0%
e 29533
 
4.9%
r 26624
 
4.5%
m 20672
 
3.5%
Other values (16) 84706
14.2%
Uppercase Letter
ValueCountFrequency (%)
K 75984
55.5%
S 21288
 
15.6%
P 10634
 
7.8%
C 10340
 
7.6%
T 4853
 
3.5%
W 4283
 
3.1%
B 1706
 
1.2%
J 1631
 
1.2%
I 1497
 
1.1%
G 895
 
0.7%
Other values (12) 3676
 
2.7%
Other Punctuation
ValueCountFrequency (%)
' 4
50.0%
. 4
50.0%
Space Separator
ValueCountFrequency (%)
1761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 734539
99.8%
Common 1769
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 107219
14.6%
n 105625
14.4%
K 75984
10.3%
g 72868
9.9%
o 47062
 
6.4%
h 39783
 
5.4%
a 34062
 
4.6%
s 29598
 
4.0%
e 29533
 
4.0%
r 26624
 
3.6%
Other values (38) 166181
22.6%
Common
ValueCountFrequency (%)
1761
99.5%
' 4
 
0.2%
. 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 736307
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 107219
14.6%
n 105625
14.3%
K 75984
10.3%
g 72868
9.9%
o 47062
 
6.4%
h 39783
 
5.4%
a 34062
 
4.6%
s 29598
 
4.0%
e 29533
 
4.0%
r 26624
 
3.6%
Other values (40) 167949
22.8%
None
ValueCountFrequency (%)
ñ 1
100.0%

City
Text

Distinct651
Distinct (%)0.5%
Missing8
Missing (%)< 0.1%
Memory size7.4 MiB
2024-08-04T10:37:07.999103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.2364882
Min length3

Characters and Unicode

Total characters1112173
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)0.1%

Sample

1st rowTumwater
2nd rowClinton
3rd rowSnohomish
4th rowSeattle
5th rowEdmonds
ValueCountFrequency (%)
seattle 23489
 
15.0%
bellevue 6960
 
4.4%
redmond 4965
 
3.2%
vancouver 4819
 
3.1%
kirkland 4201
 
2.7%
bothell 4196
 
2.7%
island 4110
 
2.6%
sammamish 3950
 
2.5%
renton 3516
 
2.2%
olympia 3228
 
2.1%
Other values (684) 93453
59.6%
2024-08-04T10:37:08.570675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 153009
13.8%
a 108553
 
9.8%
l 98753
 
8.9%
t 78416
 
7.1%
n 73266
 
6.6%
o 65038
 
5.8%
r 46419
 
4.2%
i 43967
 
4.0%
S 38974
 
3.5%
d 37887
 
3.4%
Other values (42) 367891
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 933162
83.9%
Uppercase Letter 157021
 
14.1%
Space Separator 21857
 
2.0%
Dash Punctuation 133
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 153009
16.4%
a 108553
11.6%
l 98753
10.6%
t 78416
8.4%
n 73266
 
7.9%
o 65038
 
7.0%
r 46419
 
5.0%
i 43967
 
4.7%
d 37887
 
4.1%
m 36243
 
3.9%
Other values (15) 191611
20.5%
Uppercase Letter
ValueCountFrequency (%)
S 38974
24.8%
B 20033
12.8%
R 10698
 
6.8%
K 8084
 
5.1%
M 7870
 
5.0%
L 7827
 
5.0%
V 7488
 
4.8%
I 6305
 
4.0%
T 6139
 
3.9%
P 6020
 
3.8%
Other values (15) 37583
23.9%
Space Separator
ValueCountFrequency (%)
21857
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1090183
98.0%
Common 21990
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 153009
14.0%
a 108553
 
10.0%
l 98753
 
9.1%
t 78416
 
7.2%
n 73266
 
6.7%
o 65038
 
6.0%
r 46419
 
4.3%
i 43967
 
4.0%
S 38974
 
3.6%
d 37887
 
3.5%
Other values (40) 345901
31.7%
Common
ValueCountFrequency (%)
21857
99.4%
- 133
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1112173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 153009
13.8%
a 108553
 
9.8%
l 98753
 
8.9%
t 78416
 
7.1%
n 73266
 
6.6%
o 65038
 
5.8%
r 46419
 
4.2%
i 43967
 
4.0%
S 38974
 
3.5%
d 37887
 
3.4%
Other values (42) 367891
33.1%

State
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
WA
134726 
CA
 
90
VA
 
33
MD
 
29
TX
 
18
Other values (41)
 
142

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters270076
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 134726
99.8%
CA 90
 
0.1%
VA 33
 
< 0.1%
MD 29
 
< 0.1%
TX 18
 
< 0.1%
NC 11
 
< 0.1%
CO 11
 
< 0.1%
AZ 9
 
< 0.1%
IL 7
 
< 0.1%
CT 7
 
< 0.1%
Other values (36) 97
 
0.1%

Length

2024-08-04T10:37:08.824061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa 134726
99.8%
ca 90
 
0.1%
va 33
 
< 0.1%
md 29
 
< 0.1%
tx 18
 
< 0.1%
nc 11
 
< 0.1%
co 11
 
< 0.1%
az 9
 
< 0.1%
il 7
 
< 0.1%
ct 7
 
< 0.1%
Other values (36) 97
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 134878
49.9%
W 134728
49.9%
C 132
 
< 0.1%
M 40
 
< 0.1%
V 39
 
< 0.1%
N 38
 
< 0.1%
D 37
 
< 0.1%
T 30
 
< 0.1%
O 23
 
< 0.1%
I 18
 
< 0.1%
Other values (15) 113
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270076
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 134878
49.9%
W 134728
49.9%
C 132
 
< 0.1%
M 40
 
< 0.1%
V 39
 
< 0.1%
N 38
 
< 0.1%
D 37
 
< 0.1%
T 30
 
< 0.1%
O 23
 
< 0.1%
I 18
 
< 0.1%
Other values (15) 113
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 270076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 134878
49.9%
W 134728
49.9%
C 132
 
< 0.1%
M 40
 
< 0.1%
V 39
 
< 0.1%
N 38
 
< 0.1%
D 37
 
< 0.1%
T 30
 
< 0.1%
O 23
 
< 0.1%
I 18
 
< 0.1%
Other values (15) 113
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 134878
49.9%
W 134728
49.9%
C 132
 
< 0.1%
M 40
 
< 0.1%
V 39
 
< 0.1%
N 38
 
< 0.1%
D 37
 
< 0.1%
T 30
 
< 0.1%
O 23
 
< 0.1%
I 18
 
< 0.1%
Other values (15) 113
 
< 0.1%

Postal Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct786
Distinct (%)0.6%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98171.002
Minimum1730
Maximum99701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-04T10:37:09.035707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile98006
Q198052
median98121
Q398370
95-th percentile98930
Maximum99701
Range97971
Interquartile range (IQR)318

Descriptive statistics

Standard deviation2450.3672
Coefficient of variation (CV)0.024960194
Kurtosis965.90462
Mean98171.002
Median Absolute Deviation (MAD)100
Skewness-30.280865
Sum1.325603 × 1010
Variance6004299.5
MonotonicityNot monotonic
2024-08-04T10:37:09.273335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 3482
 
2.6%
98033 2382
 
1.8%
98012 2369
 
1.8%
98004 2274
 
1.7%
98006 2192
 
1.6%
98115 2166
 
1.6%
98072 1943
 
1.4%
98040 1943
 
1.4%
98074 1929
 
1.4%
98034 1861
 
1.4%
Other values (776) 112489
83.3%
ValueCountFrequency (%)
1730 1
< 0.1%
1731 1
< 0.1%
1824 1
< 0.1%
2842 1
< 0.1%
3804 1
< 0.1%
6355 1
< 0.1%
6371 1
< 0.1%
6379 2
< 0.1%
6385 1
< 0.1%
6443 2
< 0.1%
ValueCountFrequency (%)
99701 1
 
< 0.1%
99403 46
 
< 0.1%
99402 9
 
< 0.1%
99362 277
0.2%
99361 10
 
< 0.1%
99360 4
 
< 0.1%
99357 14
 
< 0.1%
99356 1
 
< 0.1%
99354 232
0.2%
99353 176
0.1%

Model Year
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6629
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-04T10:37:09.476872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2013
Q12018
median2021
Q32022
95-th percentile2023
Maximum2024
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0016756
Coefficient of variation (CV)0.001486226
Kurtosis0.10673701
Mean2019.6629
Median Absolute Deviation (MAD)2
Skewness-0.89078006
Sum2.7273124 × 108
Variance9.0100562
MonotonicityNot monotonic
2024-08-04T10:37:09.674089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2022 27983
20.7%
2023 22476
16.6%
2021 18554
13.7%
2018 14343
10.6%
2020 11151
 
8.3%
2019 10577
 
7.8%
2017 8579
 
6.4%
2016 5688
 
4.2%
2015 4925
 
3.6%
2013 4598
 
3.4%
Other values (12) 6164
 
4.6%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 4
 
< 0.1%
2000 9
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 20
 
< 0.1%
2010 23
 
< 0.1%
2011 815
0.6%
2012 1657
1.2%
ValueCountFrequency (%)
2024 30
 
< 0.1%
2023 22476
16.6%
2022 27983
20.7%
2021 18554
13.7%
2020 11151
 
8.3%
2019 10577
 
7.8%
2018 14343
10.6%
2017 8579
 
6.4%
2016 5688
 
4.2%
2015 4925
 
3.6%

Make
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
TESLA
61808 
NISSAN
13150 
CHEVROLET
11437 
FORD
6897 
BMW
 
5895
Other values (31)
35851 

Length

Max length20
Median length14
Mean length5.5461944
Min length3

Characters and Unicode

Total characters748947
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESLA
2nd rowNISSAN
3rd rowTESLA
4th rowTESLA
5th rowTESLA

Common Values

ValueCountFrequency (%)
TESLA 61808
45.8%
NISSAN 13150
 
9.7%
CHEVROLET 11437
 
8.5%
FORD 6897
 
5.1%
BMW 5895
 
4.4%
KIA 5491
 
4.1%
TOYOTA 4883
 
3.6%
VOLKSWAGEN 3526
 
2.6%
VOLVO 3221
 
2.4%
AUDI 2727
 
2.0%
Other values (26) 16003
 
11.9%

Length

2024-08-04T10:37:09.886276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 61808
45.8%
nissan 13150
 
9.7%
chevrolet 11437
 
8.5%
ford 6897
 
5.1%
bmw 5895
 
4.4%
kia 5491
 
4.1%
toyota 4883
 
3.6%
volkswagen 3526
 
2.6%
volvo 3221
 
2.4%
audi 2727
 
2.0%
Other values (30) 16060
 
11.9%

Most occurring characters

ValueCountFrequency (%)
E 100691
13.4%
A 99357
13.3%
S 98543
13.2%
T 85511
11.4%
L 83821
11.2%
O 40723
 
5.4%
N 36882
 
4.9%
I 32476
 
4.3%
R 27887
 
3.7%
V 23254
 
3.1%
Other values (18) 119802
16.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 748109
99.9%
Dash Punctuation 777
 
0.1%
Space Separator 57
 
< 0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 100691
13.5%
A 99357
13.3%
S 98543
13.2%
T 85511
11.4%
L 83821
11.2%
O 40723
 
5.4%
N 36882
 
4.9%
I 32476
 
4.3%
R 27887
 
3.7%
V 23254
 
3.1%
Other values (15) 118964
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 777
100.0%
Space Separator
ValueCountFrequency (%)
57
100.0%
Other Punctuation
ValueCountFrequency (%)
! 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 748109
99.9%
Common 838
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 100691
13.5%
A 99357
13.3%
S 98543
13.2%
T 85511
11.4%
L 83821
11.2%
O 40723
 
5.4%
N 36882
 
4.9%
I 32476
 
4.3%
R 27887
 
3.7%
V 23254
 
3.1%
Other values (15) 118964
15.9%
Common
ValueCountFrequency (%)
- 777
92.7%
57
 
6.8%
! 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 748947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 100691
13.4%
A 99357
13.3%
S 98543
13.2%
T 85511
11.4%
L 83821
11.2%
O 40723
 
5.4%
N 36882
 
4.9%
I 32476
 
4.3%
R 27887
 
3.7%
V 23254
 
3.1%
Other values (18) 119802
16.0%

Model
Text

Distinct125
Distinct (%)0.1%
Missing249
Missing (%)0.2%
Memory size7.1 MiB
2024-08-04T10:37:10.418249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.3319336
Min length2

Characters and Unicode

Total characters853475
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowMODEL 3
2nd rowLEAF
3rd rowMODEL 3
4th rowMODEL 3
5th rowMODEL S
ValueCountFrequency (%)
model 61761
28.9%
3 25837
12.1%
y 23577
 
11.0%
leaf 13020
 
6.1%
s 7473
 
3.5%
bolt 6303
 
2.9%
ev 5649
 
2.6%
volt 4881
 
2.3%
x 4874
 
2.3%
prime 3821
 
1.8%
Other values (123) 56753
26.5%
2024-08-04T10:37:10.966113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 101889
11.9%
L 94248
11.0%
O 87279
 
10.2%
79160
 
9.3%
M 72045
 
8.4%
D 66502
 
7.8%
A 36502
 
4.3%
3 29789
 
3.5%
I 28149
 
3.3%
Y 25546
 
3.0%
Other values (28) 232366
27.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 709600
83.1%
Space Separator 79160
 
9.3%
Decimal Number 53653
 
6.3%
Dash Punctuation 8582
 
1.0%
Other Punctuation 2480
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 101889
14.4%
L 94248
13.3%
O 87279
12.3%
M 72045
10.2%
D 66502
9.4%
A 36502
 
5.1%
I 28149
 
4.0%
Y 25546
 
3.6%
R 25524
 
3.6%
T 21706
 
3.1%
Other values (15) 150210
21.2%
Decimal Number
ValueCountFrequency (%)
3 29789
55.5%
0 6048
 
11.3%
5 5647
 
10.5%
4 5482
 
10.2%
6 2563
 
4.8%
1 2105
 
3.9%
9 1204
 
2.2%
2 658
 
1.2%
8 109
 
0.2%
7 48
 
0.1%
Space Separator
ValueCountFrequency (%)
79160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8582
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 709600
83.1%
Common 143875
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 101889
14.4%
L 94248
13.3%
O 87279
12.3%
M 72045
10.2%
D 66502
9.4%
A 36502
 
5.1%
I 28149
 
4.0%
Y 25546
 
3.6%
R 25524
 
3.6%
T 21706
 
3.1%
Other values (15) 150210
21.2%
Common
ValueCountFrequency (%)
79160
55.0%
3 29789
 
20.7%
- 8582
 
6.0%
0 6048
 
4.2%
5 5647
 
3.9%
4 5482
 
3.8%
6 2563
 
1.8%
. 2480
 
1.7%
1 2105
 
1.5%
9 1204
 
0.8%
Other values (3) 815
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 853475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 101889
11.9%
L 94248
11.0%
O 87279
 
10.2%
79160
 
9.3%
M 72045
 
8.4%
D 66502
 
7.8%
A 36502
 
4.3%
3 29789
 
3.5%
I 28149
 
3.3%
Y 25546
 
3.0%
Other values (28) 232366
27.2%

Electric Vehicle Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
Battery Electric Vehicle (BEV)
103882 
Plug-in Hybrid Electric Vehicle (PHEV)
31156 

Length

Max length38
Median length30
Mean length31.845762
Min length30

Characters and Unicode

Total characters4300388
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowBattery Electric Vehicle (BEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 103882
76.9%
Plug-in Hybrid Electric Vehicle (PHEV) 31156
 
23.1%

Length

2024-08-04T10:37:11.227783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-04T10:37:11.455300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
electric 135038
23.6%
vehicle 135038
23.6%
battery 103882
18.2%
bev 103882
18.2%
plug-in 31156
 
5.5%
hybrid 31156
 
5.5%
phev 31156
 
5.5%

Most occurring characters

ValueCountFrequency (%)
e 508996
11.8%
436270
10.1%
c 405114
9.4%
t 342802
 
8.0%
i 332388
 
7.7%
l 301232
 
7.0%
V 270076
 
6.3%
r 270076
 
6.3%
E 270076
 
6.3%
B 207764
 
4.8%
Other values (13) 955594
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2690346
62.6%
Uppercase Letter 872540
 
20.3%
Space Separator 436270
 
10.1%
Open Punctuation 135038
 
3.1%
Close Punctuation 135038
 
3.1%
Dash Punctuation 31156
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 508996
18.9%
c 405114
15.1%
t 342802
12.7%
i 332388
12.4%
l 301232
11.2%
r 270076
10.0%
y 135038
 
5.0%
h 135038
 
5.0%
a 103882
 
3.9%
u 31156
 
1.2%
Other values (4) 124624
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
V 270076
31.0%
E 270076
31.0%
B 207764
23.8%
P 62312
 
7.1%
H 62312
 
7.1%
Space Separator
ValueCountFrequency (%)
436270
100.0%
Open Punctuation
ValueCountFrequency (%)
( 135038
100.0%
Close Punctuation
ValueCountFrequency (%)
) 135038
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3562886
82.9%
Common 737502
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 508996
14.3%
c 405114
11.4%
t 342802
9.6%
i 332388
9.3%
l 301232
8.5%
V 270076
7.6%
r 270076
7.6%
E 270076
7.6%
B 207764
5.8%
y 135038
 
3.8%
Other values (9) 519324
14.6%
Common
ValueCountFrequency (%)
436270
59.2%
( 135038
 
18.3%
) 135038
 
18.3%
- 31156
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 508996
11.8%
436270
10.1%
c 405114
9.4%
t 342802
 
8.0%
i 332388
 
7.7%
l 301232
 
7.0%
V 270076
 
6.3%
r 270076
 
6.3%
E 270076
 
6.3%
B 207764
 
4.8%
Other values (13) 955594
22.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
Clean Alternative Fuel Vehicle Eligible
61221 
Eligibility unknown as battery range has not been researched
56983 
Not eligible due to low battery range
16834 

Length

Max length60
Median length39
Mean length47.612205
Min length37

Characters and Unicode

Total characters6429457
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowEligibility unknown as battery range has not been researched
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowClean Alternative Fuel Vehicle Eligible
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Clean Alternative Fuel Vehicle Eligible 61221
45.3%
Eligibility unknown as battery range has not been researched 56983
42.2%
Not eligible due to low battery range 16834
 
12.5%

Length

2024-08-04T10:37:11.628534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-04T10:37:11.801196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
eligible 78055
 
8.3%
battery 73817
 
7.9%
not 73817
 
7.9%
range 73817
 
7.9%
alternative 61221
 
6.5%
clean 61221
 
6.5%
vehicle 61221
 
6.5%
fuel 61221
 
6.5%
unknown 56983
 
6.1%
as 56983
 
6.1%
Other values (7) 278434
29.7%

Most occurring characters

ValueCountFrequency (%)
e 911598
14.2%
801752
12.5%
l 531794
 
8.3%
i 506484
 
7.9%
n 481174
 
7.5%
a 441025
 
6.9%
t 417710
 
6.5%
r 322821
 
5.0%
b 265838
 
4.1%
g 208855
 
3.2%
Other values (16) 1540406
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5247783
81.6%
Space Separator 801752
 
12.5%
Uppercase Letter 379922
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 911598
17.4%
l 531794
10.1%
i 506484
9.7%
n 481174
9.2%
a 441025
8.4%
t 417710
8.0%
r 322821
 
6.2%
b 265838
 
5.1%
g 208855
 
4.0%
h 175187
 
3.3%
Other values (9) 985297
18.8%
Uppercase Letter
ValueCountFrequency (%)
E 118204
31.1%
C 61221
16.1%
V 61221
16.1%
F 61221
16.1%
A 61221
16.1%
N 16834
 
4.4%
Space Separator
ValueCountFrequency (%)
801752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5627705
87.5%
Common 801752
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 911598
16.2%
l 531794
 
9.4%
i 506484
 
9.0%
n 481174
 
8.6%
a 441025
 
7.8%
t 417710
 
7.4%
r 322821
 
5.7%
b 265838
 
4.7%
g 208855
 
3.7%
h 175187
 
3.1%
Other values (15) 1365219
24.3%
Common
ValueCountFrequency (%)
801752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6429457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 911598
14.2%
801752
12.5%
l 531794
 
8.3%
i 506484
 
7.9%
n 481174
 
7.5%
a 441025
 
6.9%
t 417710
 
6.5%
r 322821
 
5.0%
b 265838
 
4.1%
g 208855
 
3.2%
Other values (16) 1540406
24.0%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean74.591964
Minimum0
Maximum337
Zeros56983
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-04T10:37:12.016446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q3150
95-th percentile266
Maximum337
Range337
Interquartile range (IQR)150

Descriptive statistics

Standard deviation98.74412
Coefficient of variation (CV)1.3237903
Kurtosis-0.38010742
Mean74.591964
Median Absolute Deviation (MAD)21
Skewness1.0735505
Sum10072675
Variance9750.4012
MonotonicityNot monotonic
2024-08-04T10:37:12.258858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56983
42.2%
215 6440
 
4.8%
220 4163
 
3.1%
84 4041
 
3.0%
238 3509
 
2.6%
25 3244
 
2.4%
19 2548
 
1.9%
208 2504
 
1.9%
53 2491
 
1.8%
291 2372
 
1.8%
Other values (92) 46742
34.6%
ValueCountFrequency (%)
0 56983
42.2%
6 940
 
0.7%
8 39
 
< 0.1%
9 20
 
< 0.1%
10 167
 
0.1%
11 1
 
< 0.1%
12 159
 
0.1%
13 358
 
0.3%
14 1121
 
0.8%
15 89
 
0.1%
ValueCountFrequency (%)
337 76
 
0.1%
330 321
 
0.2%
322 1717
1.3%
308 507
 
0.4%
293 429
 
0.3%
291 2372
1.8%
289 624
 
0.5%
270 272
 
0.2%
266 1465
1.1%
265 127
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1448.4073
Minimum0
Maximum845000
Zeros131612
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-04T10:37:12.480702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9683.6581
Coefficient of variation (CV)6.6857285
Kurtosis475.79563
Mean1448.4073
Median Absolute Deviation (MAD)0
Skewness11.39525
Sum1.9558858 × 108
Variance93773235
MonotonicityNot monotonic
2024-08-04T10:37:12.686767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 131612
97.5%
69900 1432
 
1.1%
31950 400
 
0.3%
52900 214
 
0.2%
32250 152
 
0.1%
54950 136
 
0.1%
59900 127
 
0.1%
39995 122
 
0.1%
36900 100
 
0.1%
44100 97
 
0.1%
Other values (21) 645
 
0.5%
ValueCountFrequency (%)
0 131612
97.5%
31950 400
 
0.3%
32250 152
 
0.1%
32995 3
 
< 0.1%
33950 74
 
0.1%
34995 63
 
< 0.1%
36800 49
 
< 0.1%
36900 100
 
0.1%
39995 122
 
0.1%
43700 11
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 11
< 0.1%
110950 20
< 0.1%
109000 7
 
< 0.1%
102000 15
< 0.1%
98950 20
< 0.1%
91250 3
 
< 0.1%
90700 17
< 0.1%
89100 6
 
< 0.1%
81100 18
< 0.1%

Legislative District
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)< 0.1%
Missing312
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.504379
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-04T10:37:12.916577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118
median34
Q343
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.78378
Coefficient of variation (CV)0.50107069
Kurtosis-1.0400914
Mean29.504379
Median Absolute Deviation (MAD)11
Skewness-0.50312003
Sum3975007
Variance218.56014
MonotonicityNot monotonic
2024-08-04T10:37:13.157210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 9035
 
6.7%
45 8353
 
6.2%
48 7637
 
5.7%
36 5961
 
4.4%
1 5746
 
4.3%
5 5669
 
4.2%
46 5433
 
4.0%
43 5320
 
3.9%
11 4575
 
3.4%
37 4137
 
3.1%
Other values (39) 72860
54.0%
ValueCountFrequency (%)
1 5746
4.3%
2 1452
 
1.1%
3 666
 
0.5%
4 1048
 
0.8%
5 5669
4.2%
6 1217
 
0.9%
7 633
 
0.5%
8 1426
 
1.1%
9 735
 
0.5%
10 2389
1.8%
ValueCountFrequency (%)
49 1879
 
1.4%
48 7637
5.7%
47 2318
 
1.7%
46 5433
4.0%
45 8353
6.2%
44 3318
 
2.5%
43 5320
3.9%
42 1872
 
1.4%
41 9035
6.7%
40 3075
 
2.3%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct135038
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0634319 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-04T10:37:13.380313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.0431888 × 108
Q11.6063047 × 108
median2.0595634 × 108
Q32.3088883 × 108
95-th percentile3.5001933 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)70258358

Descriptive statistics

Standard deviation85824172
Coefficient of variation (CV)0.41592926
Kurtosis3.0864156
Mean2.0634319 × 108
Median Absolute Deviation (MAD)30456010
Skewness1.0401988
Sum2.7864172 × 1013
Variance7.3657885 × 1015
MonotonicityNot monotonic
2024-08-04T10:37:13.619092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242565116 1
 
< 0.1%
212236314 1
 
< 0.1%
339850192 1
 
< 0.1%
172424584 1
 
< 0.1%
349597314 1
 
< 0.1%
166722692 1
 
< 0.1%
248233429 1
 
< 0.1%
183414731 1
 
< 0.1%
102924538 1
 
< 0.1%
230928344 1
 
< 0.1%
Other values (135028) 135028
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
Distinct785
Distinct (%)0.6%
Missing10
Missing (%)< 0.1%
Memory size12.0 MiB
2024-08-04T10:37:13.897890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length46
Median length45
Mean length44.422468
Min length24

Characters and Unicode

Total characters5998277
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique259 ?
Unique (%)0.2%

Sample

1st rowPOINT (-122.91310169999997 47.01359260000004)
2nd rowPOINT (-122.35936399999997 47.97965520000008)
3rd rowPOINT (-122.09150499999998 47.91555500000004)
4th rowPOINT (-122.32981499999994 47.579810000000066)
5th rowPOINT (-122.37507 47.80807000000004)
ValueCountFrequency (%)
point 135028
33.3%
47.67668000000003 3482
 
0.9%
122.12301999999994 3482
 
0.9%
122.20263999999997 2382
 
0.6%
47.67850000000004 2382
 
0.6%
122.1873 2369
 
0.6%
47.82024500000006 2369
 
0.6%
122.20190499999995 2274
 
0.6%
47.61385000000007 2274
 
0.6%
122.16936999999996 2192
 
0.5%
Other values (1560) 246850
60.9%
2024-08-04T10:37:14.430084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1101383
18.4%
9 1029934
17.2%
2 391721
 
6.5%
4 358983
 
6.0%
1 310318
 
5.2%
7 294940
 
4.9%
. 270056
 
4.5%
270056
 
4.5%
5 261610
 
4.4%
6 221202
 
3.7%
Other values (10) 1488074
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4377941
73.0%
Uppercase Letter 675140
 
11.3%
Other Punctuation 270056
 
4.5%
Space Separator 270056
 
4.5%
Dash Punctuation 135028
 
2.3%
Open Punctuation 135028
 
2.3%
Close Punctuation 135028
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1101383
25.2%
9 1029934
23.5%
2 391721
 
8.9%
4 358983
 
8.2%
1 310318
 
7.1%
7 294940
 
6.7%
5 261610
 
6.0%
6 221202
 
5.1%
8 204997
 
4.7%
3 202853
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
O 135028
20.0%
T 135028
20.0%
N 135028
20.0%
I 135028
20.0%
P 135028
20.0%
Other Punctuation
ValueCountFrequency (%)
. 270056
100.0%
Space Separator
ValueCountFrequency (%)
270056
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 135028
100.0%
Open Punctuation
ValueCountFrequency (%)
( 135028
100.0%
Close Punctuation
ValueCountFrequency (%)
) 135028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5323137
88.7%
Latin 675140
 
11.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1101383
20.7%
9 1029934
19.3%
2 391721
 
7.4%
4 358983
 
6.7%
1 310318
 
5.8%
7 294940
 
5.5%
. 270056
 
5.1%
270056
 
5.1%
5 261610
 
4.9%
6 221202
 
4.2%
Other values (5) 812934
15.3%
Latin
ValueCountFrequency (%)
O 135028
20.0%
T 135028
20.0%
N 135028
20.0%
I 135028
20.0%
P 135028
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5998277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1101383
18.4%
9 1029934
17.2%
2 391721
 
6.5%
4 358983
 
6.0%
1 310318
 
5.2%
7 294940
 
4.9%
. 270056
 
4.5%
270056
 
4.5%
5 261610
 
4.4%
6 221202
 
3.7%
Other values (10) 1488074
24.8%
Distinct76
Distinct (%)0.1%
Missing8
Missing (%)< 0.1%
Memory size12.0 MiB
2024-08-04T10:37:14.701315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.450359
Min length10

Characters and Unicode

Total characters6002132
Distinct characters36
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPUGET SOUND ENERGY INC
2nd rowPUGET SOUND ENERGY INC
3rd rowPUGET SOUND ENERGY INC
4th rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
5th rowPUGET SOUND ENERGY INC
ValueCountFrequency (%)
of 128513
12.7%
120236
11.9%
wa 83452
 
8.2%
tacoma 82404
 
8.1%
sound 79899
 
7.9%
energy 79899
 
7.9%
puget 79094
 
7.8%
inc||city 49369
 
4.9%
power 29673
 
2.9%
inc 26806
 
2.6%
Other values (114) 254693
25.1%
2024-08-04T10:37:15.183268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
879008
14.6%
O 441958
 
7.4%
N 424319
 
7.1%
T 418848
 
7.0%
A 407182
 
6.8%
E 390916
 
6.5%
I 328164
 
5.5%
C 327371
 
5.5%
Y 219321
 
3.7%
U 208261
 
3.5%
Other values (26) 1956784
32.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4542749
75.7%
Space Separator 879008
 
14.6%
Math Symbol 206004
 
3.4%
Close Punctuation 116666
 
1.9%
Dash Punctuation 116666
 
1.9%
Open Punctuation 116666
 
1.9%
Decimal Number 20232
 
0.3%
Other Punctuation 4141
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 441958
 
9.7%
N 424319
 
9.3%
T 418848
 
9.2%
A 407182
 
9.0%
E 390916
 
8.6%
I 328164
 
7.2%
C 327371
 
7.2%
Y 219321
 
4.8%
U 208261
 
4.6%
G 172574
 
3.8%
Other values (14) 1203835
26.5%
Decimal Number
ValueCountFrequency (%)
1 18824
93.0%
2 575
 
2.8%
3 558
 
2.8%
5 275
 
1.4%
Other Punctuation
ValueCountFrequency (%)
& 3570
86.2%
, 296
 
7.1%
# 275
 
6.6%
Space Separator
ValueCountFrequency (%)
879008
100.0%
Math Symbol
ValueCountFrequency (%)
| 206004
100.0%
Close Punctuation
ValueCountFrequency (%)
) 116666
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 116666
100.0%
Open Punctuation
ValueCountFrequency (%)
( 116666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4542749
75.7%
Common 1459383
 
24.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 441958
 
9.7%
N 424319
 
9.3%
T 418848
 
9.2%
A 407182
 
9.0%
E 390916
 
8.6%
I 328164
 
7.2%
C 327371
 
7.2%
Y 219321
 
4.8%
U 208261
 
4.6%
G 172574
 
3.8%
Other values (14) 1203835
26.5%
Common
ValueCountFrequency (%)
879008
60.2%
| 206004
 
14.1%
) 116666
 
8.0%
- 116666
 
8.0%
( 116666
 
8.0%
1 18824
 
1.3%
& 3570
 
0.2%
2 575
 
< 0.1%
3 558
 
< 0.1%
, 296
 
< 0.1%
Other values (2) 550
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6002132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
879008
14.6%
O 441958
 
7.4%
N 424319
 
7.1%
T 418848
 
7.0%
A 407182
 
6.8%
E 390916
 
6.5%
I 328164
 
5.5%
C 327371
 
5.5%
Y 219321
 
3.7%
U 208261
 
3.5%
Other values (26) 1956784
32.6%

2020 Census Tract
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2042
Distinct (%)1.5%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2972329 × 1010
Minimum1.0810419 × 109
Maximum5.6033 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-04T10:37:15.422991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.0810419 × 109
5-th percentile5.3011041 × 1010
Q15.3033009 × 1010
median5.3033029 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.6033 × 1010
Range5.4951958 × 1010
Interquartile range (IQR)20063208

Descriptive statistics

Standard deviation1.6357832 × 109
Coefficient of variation (CV)0.030879956
Kurtosis703.09488
Mean5.2972329 × 1010
Median Absolute Deviation (MAD)27107
Skewness-26.102583
Sum7.1528535 × 1015
Variance2.6757865 × 1018
MonotonicityNot monotonic
2024-08-04T10:37:15.703133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10101086
 
0.8%
5.30330285 × 1010769
 
0.6%
5.303303232 × 1010647
 
0.5%
5.30330245 × 1010466
 
0.3%
5.303303232 × 1010460
 
0.3%
5.30330241 × 1010459
 
0.3%
5.30330093 × 1010458
 
0.3%
5.303303222 × 1010446
 
0.3%
5.30330078 × 1010445
 
0.3%
5.30330246 × 1010435
 
0.3%
Other values (2032) 129359
95.8%
ValueCountFrequency (%)
1081041901 1
< 0.1%
2090001300 1
< 0.1%
4013061064 1
< 0.1%
4013115900 1
< 0.1%
4013218000 1
< 0.1%
4013318800 1
< 0.1%
4013610301 1
< 0.1%
4013610302 1
< 0.1%
4013610500 1
< 0.1%
4013812900 1
< 0.1%
ValueCountFrequency (%)
5.60330001 × 10101
 
< 0.1%
5.60210007 × 10101
 
< 0.1%
5.307794001 × 10101
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10106
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.30770034 × 101030
< 0.1%
5.30770032 × 101029
< 0.1%
5.30770031 × 101017
< 0.1%

Interactions

2024-08-04T10:37:02.212723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:53.253888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:54.907403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:56.340019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:57.854439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:59.306345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:00.771235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:02.394849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:53.498433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:55.126223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:56.560009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:58.050903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:59.525823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:00.991321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:02.610632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:53.700623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:55.320879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:56.755594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:58.255410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:59.730416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:01.179198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:02.839139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:53.904204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:55.524017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:56.975540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:58.483312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:59.933393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:01.399443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:03.033757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:54.108293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:55.728303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:57.187510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:58.686501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:00.152691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:01.594146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:03.245357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:54.515696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:55.916013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:57.399529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:58.866860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:00.349598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:01.805301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:03.448891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:54.703419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:56.135575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:57.642642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:36:59.078868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:00.535545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-04T10:37:02.016967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-08-04T10:37:15.873409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2020 Census TractBase MSRPClean Alternative Fuel Vehicle (CAFV) EligibilityDOL Vehicle IDElectric RangeElectric Vehicle TypeLegislative DistrictMakeModel YearPostal CodeState
2020 Census Tract1.0000.0010.0050.008-0.0140.012-0.1860.0030.0010.0500.991
Base MSRP0.0011.0000.023-0.0140.0990.0220.0120.247-0.194-0.0050.047
Clean Alternative Fuel Vehicle (CAFV) Eligibility0.0050.0231.0000.3860.6590.7360.0590.5900.5100.0000.012
DOL Vehicle ID0.008-0.0140.3861.000-0.1310.096-0.0160.1100.207-0.0080.006
Electric Range-0.0140.0990.659-0.1311.0000.5380.0080.397-0.6750.0310.006
Electric Vehicle Type0.0120.0220.7360.0960.5381.0000.1030.7820.2160.0100.017
Legislative District-0.1860.0120.059-0.0160.0080.1031.0000.080-0.007-0.3461.000
Make0.0030.2470.5900.1100.3970.7820.0801.0000.2290.0000.000
Model Year0.001-0.1940.5100.207-0.6750.216-0.0070.2291.000-0.0650.036
Postal Code0.050-0.0050.000-0.0080.0310.010-0.3460.000-0.0651.0000.936
State0.9910.0470.0120.0060.0060.0171.0000.0000.0360.9361.000

Missing values

2024-08-04T10:37:03.766483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-04T10:37:04.410011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-04T10:37:05.454223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
05YJ3E1EA0KThurstonTumwaterWA98512.02019TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible220.00.022.0242565116POINT (-122.91310169999997 47.01359260000004)PUGET SOUND ENERGY INC5.306701e+10
11N4BZ1DV4NIslandClintonWA98236.02022NISSANLEAFBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.010.0183272785POINT (-122.35936399999997 47.97965520000008)PUGET SOUND ENERGY INC5.302997e+10
25YJ3E1EA0LSnohomishSnohomishWA98290.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible266.00.044.0112552366POINT (-122.09150499999998 47.91555500000004)PUGET SOUND ENERGY INC5.306105e+10
35YJ3E1EBXLKingSeattleWA98134.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible322.00.011.06336319POINT (-122.32981499999994 47.579810000000066)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+10
45YJSA1CP0DSnohomishEdmondsWA98020.02013TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible208.069900.021.0186212960POINT (-122.37507 47.80807000000004)PUGET SOUND ENERGY INC5.306105e+10
5WBY7Z8C5XJChelanMansonWA98831.02018BMWI3Plug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible97.00.012.0215122904POINT (-120.1531 47.88550500000008)PUD NO 1 OF CHELAN COUNTY5.300796e+10
65YJ3E1EC1LSnohomishMarysvilleWA98271.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible308.00.038.0110992472POINT (-122.17138469999998 48.10433000000006)PUGET SOUND ENERGY INC5.306105e+10
7WA1F2AFY7MSnohomishEdmondsWA98026.02021AUDIQ5 EPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range18.00.021.0138909032POINT (-122.33568499999996 47.803720000000055)PUGET SOUND ENERGY INC5.306105e+10
8JTDKARFPXKThurstonOlympiaWA98501.02019TOYOTAPRIUS PRIMEPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range25.00.022.0272310279POINT (-122.89691999999997 47.04353500000008)PUGET SOUND ENERGY INC5.306701e+10
91N4AZ1CP2JThurstonLaceyWA98503.02018NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible151.00.022.0235573929POINT (-122.82849999999996 47.036460000000034)PUGET SOUND ENERGY INC5.306701e+10
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
1350287SAYGDEE6PKingBellevueWA98005.02023TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.048.0235234982POINT (-122.16084999999998 47.62451500000003)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
1350291G1RD6S56HClarkVancouverWA98682.02017CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible53.00.017.0140190424POINT (-122.51464729999998 45.67862000000008)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)5.301104e+10
1350301C4JJXN68PKingSeatacWA98148.02023JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range21.00.033.0235938776POINT (-122.32806 47.46155)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
1350317SAYGDEF4NKingNorth BendWA98045.02022TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.05.0215130366POINT (-121.78140119999995 47.49353160000004)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
1350325YJ3E1EC4LSnohomishEdmondsWA98020.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible308.00.032.03315315POINT (-122.37507 47.80807000000004)PUGET SOUND ENERGY INC5.306105e+10
1350335YJSA1E29LKingYarrow PointWA98004.02020TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible330.00.048.0124776627POINT (-122.20190499999995 47.61385000000007)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
1350345YJYGDEE7MKingBurienWA98168.02021TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.033.0142857676POINT (-122.28645999999998 47.47613000000007)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303303e+10
1350355YJSA1E51NPierceGig HarborWA98335.02022TESLAMODEL SBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.026.0220157724POINT (-122.58354539999999 47.32344880000005)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY5.305307e+10
135036KM8KMDAF3PGrantEphrataWA98823.02023HYUNDAIIONIQ 5Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.013.0223872596POINT (-119.55512999999996 47.319360000000074)PUD NO 2 OF GRANT COUNTY5.302501e+10
1350371FADP5CU4EKitsapPort OrchardWA98366.02014FORDC-MAXPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range19.00.026.0171102032POINT (-122.63926499999997 47.53730000000007)PUGET SOUND ENERGY INC5.303509e+10